Publikation
Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits
Pranuthi Tenali; Sahil Sidheekh; Saurabh Mathur; Erik Blasch; Kristian Kersting; Sriraam Natarajan
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2603.26629, Pages 1-9, arXiv, 2026.
Zusammenfassung
Multimodal fusion requires integrating information
from multiple sources that may conflict depending on context.
Existing fusion approaches typically rely on static assumptions
about source reliability, limiting their ability to resolve conflicts
when a modality becomes unreliable due to situational factors
such as sensor degradation or class-specific corruption. We
introduce C2MF, a context-specfic credibility-aware multimodal
fusion framework that models per-instance source reliability
using a Conditional Probabilistic Circuit (CPC). We formalize
instance-level reliability through Context-Specific Information
Credibility (CSIC), a KL-divergence–based measure computed
exactly from the CPC. CSIC generalizes conventional static
credibility estimates as a special case, enabling principled and
adaptive reliability assessment. To evaluate robustness under
cross-modal conflicts, we propose the Conflict benchmark, in
which class-specific corruptions deliberately induce discrepancies
between different modalities. Experimental results show that
C2MF improves predictive accuracy by up to 29% over static-
reliability baselines in high-noise settings, while preserving the
interpretability advantages of probabilistic circuit-based fusion.